that past patterns in data can be used to forecast future data points. 1. Moving averages (simple moving average‚ weighted moving average): forecast is based on arithmetic average of a given number of past data points 2. Exponential smoothing (single exponential smoothing‚ double exponential smoothing) - a type of weighted moving average that allows inclusion of trends‚ etc. 3. Mathematical models (trend lines‚
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periods into the forecast and “smoothes” the data. Averaging models are computed by averaging data from several time periods and using the average as the forecast for the next time period. A moving average is an average that is updated or recomputed for every new time period being considered. The most recent information is utilized in each new moving average. This advantage is offset by the disadvantages that (1) it is difficult to choose
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Develop a 3-year moving average to forecast sales. b. Then estimate demand again with a weighted moving average in which sales in the most recent year are given a weight of 3 and a weight of 2 for the second past year and sales in the other 2 years are each given a weight of 1. c. Which method do you think is best? In this case‚ the 3 year moving average is the better method as the Mean Absolute Deviation (MAD) is only 3.042 as compared to 3.347 for the weighted moving average method. What
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600 | a. Use a 2-period moving average to forecast the population of the United States in 2003. [pic] b. Use a 3-period moving average to forecast the population of the United States in 2003 c. Which averaging period provides a better historical fit based on the MAD criterion? [pic] 2. Refer to the data provided in problem 1. Use a 3-period weighted moving average to forecast the population of the United States in 2003.
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forecast error: Average error Mean absolute deviation (MAD) Average absolute error Mean squared error (MSE) Average of squared error Mean Absolute Percent error (MAPE) Tracking signal Ratio of cumulative error and MAD Time Series Forecasting Naïve (Just move the At value over 1 and down 1 to the Ft column) Moving Average Weighted Moving Average Exponential Smoothing Trend Adjusted Forecasting Moving Average N=3 (493+498+492)/3=494.33 Weighted Moving Average .2‚ .3‚.5 (.2*493)+(
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influence (Multiplicative Model) 2 Smoothing Methods Smoothing methods are used to average out the irregular components of the time series in cases where the time series: is fairly stable‚ and has no significant trend‚ seasonal‚ or cyclical effects. • • Four common smoothing methods: 1) 2) 3) 4) Moving Average Weighted Moving Averages Exponential Smoothing Centered Moving Average (not for forecasting as we will see later – only a process to lead to forecasting) Measures
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Chapter 4: Multiple Choice Questions 1. Forecasts a. become more accurate with longer time horizons b. are rarely perfect c. are more accurate for individual items than for groups of items d. all of the above e. none of the above One purpose of short-range forecasts is to determine a. production planning b. inventory budgets c. research and development plans d. facility location e. job assignments Forecasts are usually classified by time horizon into three categories a. short-range‚ medium-range
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For example‚ raw materials for the leather jackets need to be ordered 8 months ahead. And‚ in the short term‚ food and labor for daily operations should be forecasted. Hard Rock uses many of the forecasting techniques as: moving averages‚ weighted moving averages‚ exponential smoothing‚ and regression analysis. They start forecasting at the unit level every month‚ then take it to the quarter‚ and then to a year. All this data is compared to previous years and to the budget expectation to make
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Forecasting Models: Associative and Time Series Forecasting involves using past data to generate a number‚ set of numbers‚ or scenario that corresponds to a future occurrence. It is absolutely essential to short-range and long-range planning. Time Series and Associative models are both quantitative forecast techniques are more objective than qualitative techniques such as the Delphi Technique and market research. Time Series Models Based on the assumption that history will repeat itself‚
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period’s actual value as a forecast Simple Mean (Average) Uses an average of all past data as a forecast Simple Moving Average Uses an average of a specified number of the most recent observations‚ with each observation receiving the same emphasis (weight) Weighted Moving Average Uses an average of a specified number of the most recent observations‚ with each observation receiving a different emphasis (weight) Exponential Smoothing A weighted average procedure with weights declining exponentially
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